#include "tensorflow/core/ir/utils/shape_inference_utils.h"

namespace mlir {

class Operation;

namespace TF {

// Runs TensorFlow shape inference associated to the op type registered in the
// TensorFlow op registry based on the Graph version, operands, and attributes.
// Invoking this shape function will create conversions of parameters to the
// TensorFlow Graph equivalent data structures and back to MLIR equivalent data

          password: ${{ secrets.DOCKERHUB_TOKEN }}
      -
        name: Login to GCR
        uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a # v2.1.0
        with:
          registry: gcr.io
          username: _json_key
          password: ${{ secrets.GCP_CREDS }}
      -
        name: Generate variables for cache busting and tag naming
        run: |

    return "tfl-device-transform-nnapi";
  }
  llvm::StringRef getDescription() const final {
    return "Suitable transformation for nnapi only.";
  }
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect>();
  }
  void runOnOperation() override;
};

void DeviceTransformNNAPIPass::runOnOperation() {
  auto func = getOperation();
  auto* ctx = &getContext();

#include "mlir/IR/DialectRegistry.h"  // from @llvm-project

namespace mlir {

// Inserts common Tensorflow dialects used for offline tools.
void RegisterCommonToolingDialects(mlir::DialectRegistry& registry);

};  // namespace mlir

    std::vector<tensorflow::GraphOptimizationPass*> tf_passes);

// Same as above but pass names instead of the passes provided. The registered
// passes are queried, if a TF graph optimization pass is not found in registry
// then the pass fails.
// Pass requires that the module ran on is convertible to TF Graph.
std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>>
CreateTensorFlowGraphOptimizationPass(

  absl::flat_hash_set<std::string> denylisted_mlir_op_names;
  for (auto& entry : denylisted_ops) {
    denylisted_mlir_op_names.insert(TfLiteToMlir(entry));
  }

  DialectRegistry registry;
  MLIRContext context(registry);
  StatusScopedDiagnosticHandler statusHandler(&context,
                                              /*propagate=*/true);

  // Import input_model to a MLIR module

class LowerGlobalsToMlProgram
    : public impl::LowerGlobalsToMlProgramPassBase<LowerGlobalsToMlProgram> {
 public:
  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<mlir::tf_saved_model::TensorFlowSavedModelDialect,
                    ml_program::MLProgramDialect>();
  }

  void runOnOperation() override {

  std::string saved_model_mlir_path = tensorflow::GetDataDependencyFilepath(
      "tensorflow/compiler/mlir/tfrt/tests/saved_model/testdata/test.mlir");

  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialects(registry);
  mlir::MLIRContext context(registry);
  auto module =
      mlir::parseSourceFile<mlir::ModuleOp>(saved_model_mlir_path, &context);
  ASSERT_TRUE(module);

  std::vector<std::string> inputs;

PYBIND11_MODULE(mlir_wrapper, m) {
  m.def("preloadTensorFlowDialects", [](mlir::MLIRContext &context) {
    mlir::DialectRegistry registry;
    mlir::RegisterAllTensorFlowDialects(registry);
    context.appendDialectRegistry(registry);
    context.loadAllAvailableDialects();
  });

  m.def("verify", [](std::string input) {
    llvm::SourceMgr SM = llvm::SourceMgr();

      "tensorflow/compiler/mlir/tfrt/tests/analysis/testdata/test.mlir");

  mlir::DialectRegistry registry;
  tfrt::RegisterTFRTDialects(registry);
  registry.insert<tfrt::fallback_async::FallbackAsyncDialect>();
  registry.insert<tfrt::fallback_sync::FallbackSyncDialect>();
  mlir::MLIRContext context(registry);
  auto module =
      mlir::parseSourceFile<mlir::ModuleOp>(saved_model_mlir_path, &context);
  ASSERT_TRUE(module);